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chore: update mllama to use ollama engine (#10637)

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This commit is contained in:
Michael Yang
2025-05-13 17:36:02 -07:00
committed by GitHub
parent 0478d440f0
commit 23125648b8
67 changed files with 785 additions and 4354 deletions
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2
llama/patches/0005-solar-pro.patch
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@@ -270,7 +270,7 @@ index 3a4e72a3..831b68c0 100644
+ // self-attention
+ {
+ // rope freq factors for llama3; may return nullptr for llama2 and other models
+ ggml_tensor * rope_factors = static_cast<const llama_kv_cache_unified *>(memory)->cbs.get_rope_factors(n_ctx_per_seq, il);
+ ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il);
+
+ // compute Q and K and RoPE them
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);

1027
llama/patches/0006-add-mllama-support.patch
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File diff suppressed because it is too large Load Diff

0
llama/patches/0008-fix-deepseek-deseret-regex.patch → llama/patches/0006-fix-deepseek-deseret-regex.patch
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419
llama/patches/0007-add-unpad-operator.patch
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@@ -1,419 +0,0 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Sun, 13 Apr 2025 22:10:06 -0400
Subject: [PATCH] add unpad operator
adds the unpad operator to GGML
---
ggml/include/ggml.h | 10 +++++
ggml/src/ggml-cpu/ggml-cpu.c | 5 +++
ggml/src/ggml-cpu/ops.cpp | 55 ++++++++++++++++++++++++++++
ggml/src/ggml-cpu/ops.h | 1 +
ggml/src/ggml-cuda/ggml-cuda.cu | 4 ++
ggml/src/ggml-cuda/pad.cu | 46 +++++++++++++++++++++++
ggml/src/ggml-cuda/pad.cuh | 1 +
ggml/src/ggml-metal/ggml-metal.m | 33 +++++++++++++++++
ggml/src/ggml-metal/ggml-metal.metal | 45 +++++++++++++++++++++++
ggml/src/ggml.c | 25 ++++++++++++-
10 files changed, 223 insertions(+), 2 deletions(-)
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index e91dedf1..8dc107ba 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -489,6 +489,7 @@ extern "C" {
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_PAD_REFLECT_1D,
+ GGML_OP_UNPAD,
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
@@ -1781,6 +1782,15 @@ extern "C" {
int p0,
int p1);
+ // unpad each dimension: [x, ..., x, y, ..., y] -> [x, ..., x]
+ GGML_API struct ggml_tensor * ggml_unpad(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int p0,
+ int p1,
+ int p2,
+ int p3);
+
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
// timesteps: [N,]
// return: [N, dim]
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index a30e67f2..835e6495 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1951,6 +1951,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad_reflect_1d(params, tensor);
} break;
+ case GGML_OP_UNPAD:
+ {
+ ggml_compute_forward_unpad(params, tensor);
+ } break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@@ -2274,6 +2278,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
+ case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 955fec59..1868a10c 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -6690,6 +6690,61 @@ void ggml_compute_forward_pad_reflect_1d(
}
}
+// ggml_compute_forward_unpad
+
+static void ggml_compute_forward_unpad_f32(
+ const struct ggml_compute_params *params,
+ struct ggml_tensor *dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ GGML_ASSERT(src0->nb[0] == sizeof(float));
+ GGML_ASSERT( dst->nb[0] == sizeof(float));
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ GGML_TENSOR_UNARY_OP_LOCALS
+
+ float * dst_ptr = (float *) dst->data;
+
+ // TODO: optimize
+
+ for (int64_t i2 = 0; i2 < ne2; ++i2) {
+ for (int64_t i1 = ith; i1 < ne1; i1 += nth) {
+ for (int64_t i0 = 0; i0 < ne0; ++i0) {
+ for (int64_t i3 = 0; i3 < ne3; ++i3) {
+ const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+
+ const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+
+ if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+ dst_ptr[dst_idx] = *src_ptr;
+ }
+ }
+ }
+ }
+ }
+}
+
+void ggml_compute_forward_unpad(
+ const struct ggml_compute_params * params,
+ struct ggml_tensor * dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ switch (src0->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_unpad_f32(params, dst);
+ } break;
+ default:
+ {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
// ggml_compute_forward_arange
static void ggml_compute_forward_arange_f32(
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index dc081b9e..a7125555 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -72,6 +72,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params
void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_unpad(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index cb0d8528..6fe86674 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2238,6 +2238,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_PAD:
ggml_cuda_op_pad(ctx, dst);
break;
+ case GGML_OP_UNPAD:
+ ggml_cuda_op_unpad(ctx, dst);
+ break;
case GGML_OP_ARANGE:
ggml_cuda_op_arange(ctx, dst);
break;
@@ -3212,6 +3215,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_PAD:
+ case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_LEAKY_RELU:
diff --git a/ggml/src/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu
index 77432b04..7d45a7e1 100644
--- a/ggml/src/ggml-cuda/pad.cu
+++ b/ggml/src/ggml-cuda/pad.cu
@@ -47,3 +47,49 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
}
+
+static __global__ void unpad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
+ // blockIdx.z: idx of ne2*ne3, aka ne02*ne03
+ // blockIdx.y: idx of ne1
+ // blockIDx.x: idx of ne0 / BLOCK_SIZE
+ int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+ if (nidx >= ne0) {
+ return;
+ }
+
+ // operation
+ int offset_dst =
+ nidx +
+ blockIdx.y * ne0 +
+ blockIdx.z * ne0 * gridDim.y;
+ if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) {
+ int offset_src =
+ nidx +
+ blockIdx.y * ne00 +
+ blockIdx.z * ne00 * ne01;
+ dst[offset_dst] = x[offset_src];
+ }
+}
+
+static void unpad_f32_cuda(const float * x, float * dst,
+ const int ne00, const int ne01, const int ne02, const int ne03,
+ const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
+ int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
+ dim3 gridDim(num_blocks, ne1, ne2*ne3);
+ unpad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
+}
+
+void ggml_cuda_op_unpad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * src0 = dst->src[0];
+ const float * src0_d = (const float *)src0->data;
+ float * dst_d = (float *)dst->data;
+ cudaStream_t stream = ctx.stream();
+
+ GGML_ASSERT(src0->type == GGML_TYPE_F32);
+ GGML_ASSERT(dst->type == GGML_TYPE_F32);
+ GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+ unpad_f32_cuda(src0_d, dst_d,
+ src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+ dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
+}
\ No newline at end of file
diff --git a/ggml/src/ggml-cuda/pad.cuh b/ggml/src/ggml-cuda/pad.cuh
index 8fd386b0..e2ededc3 100644
--- a/ggml/src/ggml-cuda/pad.cuh
+++ b/ggml/src/ggml-cuda/pad.cuh
@@ -3,3 +3,4 @@
#define CUDA_PAD_BLOCK_SIZE 256
void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_unpad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 1b56f858..7641247e 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -347,6 +347,7 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
GGML_METAL_KERNEL_TYPE_PAD_F32,
GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32,
+ GGML_METAL_KERNEL_TYPE_UNPAD_F32,
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
@@ -1294,6 +1295,7 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32, pad_reflect_1d_f32, true);
+ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UNPAD_F32, unpad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
@@ -1655,6 +1657,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_OP_POOL_2D:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
+ case GGML_OP_UNPAD:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
case GGML_OP_LEAKY_RELU:
@@ -4184,6 +4187,36 @@ static bool ggml_metal_encode_node(
const int nth = MIN(1024, ne0);
+ [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+ } break;
+ case GGML_OP_UNPAD:
+ {
+ GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+ id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UNPAD_F32].pipeline;
+
+ [encoder setComputePipelineState:pipeline];
+ [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+ [encoder setBuffer:id_dst offset:offs_dst atIndex:1];
+ [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+ [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+ [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+ [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
+ [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+ [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+ [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+ [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
+ [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10];
+ [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11];
+ [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12];
+ [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13];
+ [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14];
+ [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
+ [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
+ [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
+
+ const int nth = MIN(1024, ne0);
+
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ARANGE:
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 9cfddf45..080a943b 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -3121,6 +3121,51 @@ kernel void kernel_pad_reflect_1d_f32(
}
}
+kernel void kernel_unpad_f32(
+ device const char * src0,
+ device char * dst,
+ constant int64_t & ne00,
+ constant int64_t & ne01,
+ constant int64_t & ne02,
+ constant int64_t & ne03,
+ constant uint64_t & nb00,
+ constant uint64_t & nb01,
+ constant uint64_t & nb02,
+ constant uint64_t & nb03,
+ constant int64_t & ne0,
+ constant int64_t & ne1,
+ constant int64_t & ne2,
+ constant int64_t & ne3,
+ constant uint64_t & nb0,
+ constant uint64_t & nb1,
+ constant uint64_t & nb2,
+ constant uint64_t & nb3,
+ uint3 tgpig[[threadgroup_position_in_grid]],
+ uint3 tpitg[[thread_position_in_threadgroup]],
+ uint3 ntg[[threads_per_threadgroup]]) {
+
+ const int64_t i3 = tgpig.z;
+ const int64_t i2 = tgpig.y;
+ const int64_t i1 = tgpig.x;
+
+ const int64_t i03 = i3;
+ const int64_t i02 = i2;
+ const int64_t i01 = i1;
+
+ device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01);
+ device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1);
+
+ if (i1 < ne01 && i2 < ne02 && i3 < ne03) {
+ for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
+ if (i0 < ne00) {
+ dst_ptr[i0] = src0_ptr[i0];
+ }
+ }
+
+ return;
+ }
+}
+
kernel void kernel_arange_f32(
device char * dst,
constant ggml_metal_kargs_arange & args,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 8a654624..6b034d35 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -923,6 +923,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"UPSCALE",
"PAD",
"PAD_REFLECT_1D",
+ "UNPAD",
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
@@ -953,7 +954,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -1018,6 +1019,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"upscale(x)",
"pad(x)",
"pad_reflect_1d(x)",
+ "unpad(x)",
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
@@ -1048,7 +1050,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -4274,6 +4276,25 @@ struct ggml_tensor * ggml_pad_reflect_1d(
return result;
}
+// ggml_unpad
+
+struct ggml_tensor * ggml_unpad(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int p0, int p1, int p2, int p3) {
+
+ struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
+ a->ne[0] - p0,
+ a->ne[1] - p1,
+ a->ne[2] - p2,
+ a->ne[3] - p3);
+
+ result->op = GGML_OP_UNPAD;
+ result->src[0] = a;
+
+ return result;
+}
+
// ggml_arange
struct ggml_tensor * ggml_arange(

0
llama/patches/0009-maintain-ordering-for-rules-for-grammar.patch → llama/patches/0007-maintain-ordering-for-rules-for-grammar.patch
View File

26
llama/patches/0010-ensure-KV-cache-is-fully-defragmented.patch → llama/patches/0008-ensure-KV-cache-is-fully-defragmented.patch
View File

@@ -58,7 +58,7 @@ index c22687e4..c5948e8f 100644
auto * gf = graph_init();
diff --git a/src/llama-context.h b/src/llama-context.h
index c4ab242a..9970dfc6 100644
index c0ceacb1..0264e937 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -5,6 +5,7 @@
@@ -70,10 +70,10 @@ index c4ab242a..9970dfc6 100644
#include "ggml-cpp.h"
#include "ggml-opt.h"
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index a7b0a7eb..1a50c034 100644
index 3dcad65b..60e67b03 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -372,8 +372,6 @@ void llama_kv_cache_unified::commit() {
@@ -364,8 +364,6 @@ void llama_kv_cache_unified::commit() {
}
bool llama_kv_cache_unified::update(llama_context & lctx) {
@@ -82,7 +82,7 @@ index a7b0a7eb..1a50c034 100644
auto * sched = lctx.get_sched();
if (has_shift) {
@@ -396,8 +394,6 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
@@ -388,8 +386,6 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
res->set_inputs(nullptr);
lctx.graph_compute(gf, false);
@@ -91,7 +91,7 @@ index a7b0a7eb..1a50c034 100644
}
{
@@ -411,27 +407,36 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
@@ -403,27 +399,36 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
if (do_defrag) {
LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
@@ -133,7 +133,7 @@ index a7b0a7eb..1a50c034 100644
}
void llama_kv_cache_unified::defrag_sched(float thold) {
@@ -715,11 +720,10 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift(
@@ -707,11 +712,10 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift(
llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
const llama_cparams & cparams,
ggml_context * ctx,
@@ -147,7 +147,7 @@ index a7b0a7eb..1a50c034 100644
#if 0
// CPU defrag
//
@@ -791,32 +795,20 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
@@ -783,32 +787,20 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size());
}
#else
@@ -185,7 +185,7 @@ index a7b0a7eb..1a50c034 100644
ggml_tensor * view_v_src;
ggml_tensor * view_v_dst;
@@ -824,31 +816,29 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
@@ -816,31 +808,29 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
if (cparams.flash_attn) {
// NOTE: the V cache is not transposed when using flash attention
view_v_src = ggml_view_2d(ctx, v_l[il],
@@ -225,7 +225,7 @@ index a7b0a7eb..1a50c034 100644
}
//LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
@@ -865,17 +855,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
@@ -857,17 +847,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
assert(n_used <= n_kv);
@@ -244,7 +244,7 @@ index a7b0a7eb..1a50c034 100644
// determine which KV cells to move where
//
@@ -883,10 +863,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
@@ -875,10 +855,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
//
// if ids[i] == i || ids[i] == n_kv, then cell i is not moved
//
@@ -256,7 +256,7 @@ index a7b0a7eb..1a50c034 100644
for (uint32_t i0 = 0; i0 < n_used; ++i0) {
const auto & cell0 = cells[i0];
@@ -935,19 +912,11 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
@@ -927,19 +904,11 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
// are we moving a continuous block of memory?
bool cont = false;
@@ -276,7 +276,7 @@ index a7b0a7eb..1a50c034 100644
cont = false;
continue;
}
@@ -963,8 +932,10 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
@@ -955,8 +924,10 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
head = n_used;
if (!cont) {
@@ -288,7 +288,7 @@ index a7b0a7eb..1a50c034 100644
}
nf++;
@@ -974,22 +945,16 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
@@ -966,22 +937,16 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
}
}

2
llama/patches/0011-sort-devices-by-score.patch → llama/patches/0009-sort-devices-by-score.patch
View File

@@ -11,7 +11,7 @@ with the fastest acceleration is loaded
1 file changed, 13 insertions(+), 8 deletions(-)
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 82ae1b5b..1487f322 100644
index 405d8e31..4e67d243 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -157,7 +157,7 @@ struct ggml_backend_reg_entry {

0
llama/patches/0012-add-phony-target-ggml-cpu-for-all-cpu-variants.patch → llama/patches/0010-add-phony-target-ggml-cpu-for-all-cpu-variants.patch
View File

0
llama/patches/0013-remove-amx.patch → llama/patches/0011-remove-amx.patch
View File

0
llama/patches/0014-fix-string-arr-kv-loading.patch → llama/patches/0012-fix-string-arr-kv-loading.patch
View File

4
llama/patches/0015-ollama-debug-tensor.patch → llama/patches/0013-ollama-debug-tensor.patch
View File

@@ -8,7 +8,7 @@ Subject: [PATCH] ollama debug tensor
1 file changed, 6 insertions(+)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 835e6495..3902894b 100644
index a30e67f2..2462d2b8 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -15,6 +15,8 @@
@@ -20,7 +20,7 @@ index 835e6495..3902894b 100644
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
@@ -2846,6 +2848,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
@@ -2841,6 +2843,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
ggml_compute_forward(&params, node);

0
llama/patches/0016-add-ollama-vocab-for-grammar-support.patch → llama/patches/0014-add-ollama-vocab-for-grammar-support.patch
View File